Optimization designs and performance comparison of two CUSUM schemes for monitoring process shifts in mean and variance

In statistical process control (SPC), when dealing with a quality characteristic x that is a variable, it is usually necessary to monitor both the mean value and variability. This article proposes an optimization algorithm (called the holistic algorithm) to design the CUSUM charts for this purpose. It facilitates the determination of the charting parameters of the CUSUM charts and considerably or significantly increases their overall detection effectiveness. A single CUSUM chart (called the ABS CUSUM chart) has been developed by the holistic algorithm and fully investigated. This chart is able to detect two-sided mean shifts and increasing variance shifts by inspecting the absolute value of sample mean shift. The results of performance studies show that the overall performance of the ABS CUSUM chart is nearly as good as an optimal 3-CUSUM scheme (a scheme incorporating three individual CUSUM charts). However, since the ABS CUSUM chart is easier for implementation and design, it may be more suitable for many SPC applications in which both mean and variance of a variable have to be monitored.

[1]  William H. Woodall,et al.  CUSUM charts with variable sampling intervals , 1990 .

[2]  Wei Jiang,et al.  A Markov Chain Model for the Adaptive CUSUM Control Chart , 2006 .

[3]  Dov Ingman,et al.  Adaptive Control Limits for Bivariate Process Monitoring , 1996 .

[4]  Zhang Wu,et al.  A single CUSUM chart using a single observation to monitor a variable , 2007 .

[5]  A. Ravindran,et al.  Engineering Optimization: Methods and Applications , 2006 .

[6]  Francisco Aparisi,et al.  Synthetic-X control charts optimized for in-control and out-of-control regions , 2009, Comput. Oper. Res..

[7]  Sheng Zhang,et al.  A CUSUM scheme with variable sample sizes and sampling intervals for monitoring the process mean and variance , 2007, Qual. Reliab. Eng. Int..

[8]  Ronald B. Crosier,et al.  A new two-sided cumulative sum quality control scheme , 1986 .

[9]  Ross Sparks,et al.  CUSUM Charts for Signalling Varying Location Shifts , 2000 .

[10]  Ken Nishina,et al.  A comparison of control charts from the viewpoint of change‐point estimation , 1992 .

[11]  David He,et al.  Joint statistical design of double sampling X and s charts , 2006, Eur. J. Oper. Res..

[12]  Douglas C. Montgomery,et al.  The Economic Design of Control Charts: A Review and Literature Survey , 1980 .

[13]  Yu Tian,et al.  Optimization design of the charts for monitoring process capability , 2002 .

[14]  H. A. Knappenberger,et al.  Minimum Cost Quality Control Tests , 1969 .

[15]  William H. Woodall,et al.  The Statistical Design of Quality Control Charts , 1985 .

[16]  Bassim Mansour,et al.  MINIMUM COST QUALITY CONTROL TESTS , 2008, IRAQI JOURNAL OF STATISTICAL SCIENCES.

[17]  Marion R. Reynolds,et al.  Comparisons of Some Exponentially Weighted Moving Average Control Charts for Monitoring the Process Mean and Variance , 2006, Technometrics.

[18]  Douglas C. Montgomery,et al.  Research Issues and Ideas in Statistical Process Control , 1999 .

[19]  Rickie J. Domangue,et al.  Some omnibus exponentially weighted moving average statistical process monitoring schemes , 1991 .

[20]  H. Saunders,et al.  Probabilistic Engineering Design—Principles and Applications , 1987 .

[21]  Y. K. Chen,et al.  Design of EWMA and CUSUM control charts subject to random shift sizes and quality impacts , 2007 .

[22]  Shey-Huei Sheu,et al.  Monitoring autocorrelated Process Mean and Variance Using a Gwma Chart Based on residuals , 2008, Asia Pac. J. Oper. Res..

[23]  Smiley W. Cheng,et al.  Monitoring Process Mean and Variability with One EWMA Chart , 2001 .

[24]  D. Hawkins A Cusum for a Scale Parameter , 1981 .

[25]  Marion R. Reynolds,et al.  Control Charts and the Efficient Allocation of Sampling Resources , 2004, Technometrics.

[26]  Erwin M. Saniga,et al.  Economic Statistical Control-Chart Designs With an Application to and R Charts , 1989 .

[27]  Yu-Chang Lin,et al.  Non-normality and the variable parameters X control charts , 2007, Eur. J. Oper. Res..

[28]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[29]  Chao-Yu Chou,et al.  Economic-statistical design of X ¥ charts for non-normal data by considering quality loss , 2000 .

[30]  Herbert Moskowitz,et al.  Joint economic design of EWMA control charts for mean and variance , 2008, Eur. J. Oper. Res..

[31]  William H. Woodall,et al.  A Control Chart for Preliminary Analysis of Individual Observations , 1996 .

[32]  E. Saniga Economic Statistical Control-Chart Designs with an Application to X̄ and R Charts@@@Economic Statistical Control-Chart Designs with an Application to X and R Charts , 1989 .

[33]  Yu Tian,et al.  Weighted-loss-function CUSUM chart for monitoring mean and variance of a production process , 2005 .

[34]  A. Duncan The Economic Design of -Charts When There is a Multiplicity of Assignable Causes , 1971 .

[35]  Elsayed A. Elsayed,et al.  An economic design of [xbar] control chart using quadratic loss function , 1994 .

[36]  Marion R. Reynolds,et al.  Should Observations Be Grouped for Effective Process Monitoring? , 2004 .